In the literature of oil and gas field development and production optimization, different optimization algorithms have been applied to solve different aspects of the optimization problem. The well control or production optimization problem in which the time-varying operational settings of existing wells are optimized has been efficiently solved with gradient-based methods [1]}, [2]}, [3]}, [4]}, ensemble-based methods [5]}, [6]} or with efficient proxies like reduced-order models [7]}, [8]}. For the well placement optimization, popular algorithms such as the genetic algorithms and the particle swarm optimization algorithms [9]}, [10]}, [11]}, [12]}, [13]} typically entail thousands of simulation runs to get improved result with no guarantee of global optimality. The joint well placement and production optimization problems have also been considered [14]}, [15]}, [13]}, [12]}, [18]}, which typically require even larger numbers of simulations. In addition, while the approaches considered in these studies may provide satisfactory results for the field development optimization problem, the solution obtained in each case is tied to a specific field development scenario. If the economic conditions or geological models used in the optimization change, the optimization process needs to be repeated. In other words, the solution from traditional optimization methods lacks the ability to generalize when the underlying scenario changes. It should be noted that the ability to generalize that is discussed here is different from the robust optimization as studied in [19]}, [20]}, in which the optimization is performed under uncertainty. While the solutions from robust optimization accounts for the uncertainty in the the model parameters, they still don't generalize when the ranges of those uncertainties change.
